library(tidyr)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(ggplot2)
library(plyr)

## -------------------------------------------------------------------------

## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)

## -------------------------------------------------------------------------

## 
## Attaching package: 'plyr'

## The following objects are masked from 'package:dplyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize

library(stringr)
library(imputeTS)
library(DataExplorer)
library(cbanalysis)
library(gvlma)
require(knitr)

## Loading required package: knitr

require(lubridate)

## Loading required package: lubridate

## 
## Attaching package: 'lubridate'

## The following object is masked from 'package:plyr':
## 
##     here

## The following object is masked from 'package:base':
## 
##     date

moneyball <- read.csv("https://raw.githubusercontent.com/xkong100/data-621/master/Hw1/moneyball-training-data.csv", stringsAsFactors = FALSE, check.names = FALSE, na.strings = c("", "NA"))
kable(head(moneyball))

INDEX	TARGET_WINS	TEAM_BATTING_H	TEAM_BATTING_2B	TEAM_BATTING_3B	TEAM_BATTING_HR	TEAM_BATTING_BB	TEAM_BATTING_SO	TEAM_BASERUN_SB	TEAM_BASERUN_CS	TEAM_BATTING_HBP	TEAM_PITCHING_H	TEAM_PITCHING_HR	TEAM_PITCHING_BB	TEAM_PITCHING_SO	TEAM_FIELDING_E	TEAM_FIELDING_DP
1	39	1445	194	39	13	143	842	NA	NA	NA	9364	84	927	5456	1011	NA
2	70	1339	219	22	190	685	1075	37	28	NA	1347	191	689	1082	193	155
3	86	1377	232	35	137	602	917	46	27	NA	1377	137	602	917	175	153
4	70	1387	209	38	96	451	922	43	30	NA	1396	97	454	928	164	156
5	82	1297	186	27	102	472	920	49	39	NA	1297	102	472	920	138	168
6	75	1279	200	36	92	443	973	107	59	NA	1279	92	443	973	123	149

nrow(moneyball)

## [1] 2276

ncol(moneyball)

## [1] 17

cleanNames <- function(df) {
    name_list <- names(df)
    name_list <- gsub("TEAM_", "", name_list)
    names(df) <- name_list
    df
}

moneyball <- cleanNames(moneyball)
kable(head(moneyball))

INDEX	TARGET_WINS	BATTING_H	BATTING_2B	BATTING_3B	BATTING_HR	BATTING_BB	BATTING_SO	BASERUN_SB	BASERUN_CS	BATTING_HBP	PITCHING_H	PITCHING_HR	PITCHING_BB	PITCHING_SO	FIELDING_E	FIELDING_DP
1	39	1445	194	39	13	143	842	NA	NA	NA	9364	84	927	5456	1011	NA
2	70	1339	219	22	190	685	1075	37	28	NA	1347	191	689	1082	193	155
3	86	1377	232	35	137	602	917	46	27	NA	1377	137	602	917	175	153
4	70	1387	209	38	96	451	922	43	30	NA	1396	97	454	928	164	156
5	82	1297	186	27	102	472	920	49	39	NA	1297	102	472	920	138	168
6	75	1279	200	36	92	443	973	107	59	NA	1279	92	443	973	123	149
There ar	e 2276 rows an	d 16 columns	.

summary(moneyball)

##      INDEX         TARGET_WINS       BATTING_H      BATTING_2B   
##  Min.   :   1.0   Min.   :  0.00   Min.   : 891   Min.   : 69.0  
##  1st Qu.: 630.8   1st Qu.: 71.00   1st Qu.:1383   1st Qu.:208.0  
##  Median :1270.5   Median : 82.00   Median :1454   Median :238.0  
##  Mean   :1268.5   Mean   : 80.79   Mean   :1469   Mean   :241.2  
##  3rd Qu.:1915.5   3rd Qu.: 92.00   3rd Qu.:1537   3rd Qu.:273.0  
##  Max.   :2535.0   Max.   :146.00   Max.   :2554   Max.   :458.0  
##                                                                  
##    BATTING_3B       BATTING_HR       BATTING_BB      BATTING_SO    
##  Min.   :  0.00   Min.   :  0.00   Min.   :  0.0   Min.   :   0.0  
##  1st Qu.: 34.00   1st Qu.: 42.00   1st Qu.:451.0   1st Qu.: 548.0  
##  Median : 47.00   Median :102.00   Median :512.0   Median : 750.0  
##  Mean   : 55.25   Mean   : 99.61   Mean   :501.6   Mean   : 735.6  
##  3rd Qu.: 72.00   3rd Qu.:147.00   3rd Qu.:580.0   3rd Qu.: 930.0  
##  Max.   :223.00   Max.   :264.00   Max.   :878.0   Max.   :1399.0  
##                                                    NA's   :102     
##    BASERUN_SB      BASERUN_CS     BATTING_HBP      PITCHING_H   
##  Min.   :  0.0   Min.   :  0.0   Min.   :29.00   Min.   : 1137  
##  1st Qu.: 66.0   1st Qu.: 38.0   1st Qu.:50.50   1st Qu.: 1419  
##  Median :101.0   Median : 49.0   Median :58.00   Median : 1518  
##  Mean   :124.8   Mean   : 52.8   Mean   :59.36   Mean   : 1779  
##  3rd Qu.:156.0   3rd Qu.: 62.0   3rd Qu.:67.00   3rd Qu.: 1682  
##  Max.   :697.0   Max.   :201.0   Max.   :95.00   Max.   :30132  
##  NA's   :131     NA's   :772     NA's   :2085                   
##   PITCHING_HR     PITCHING_BB      PITCHING_SO        FIELDING_E    
##  Min.   :  0.0   Min.   :   0.0   Min.   :    0.0   Min.   :  65.0  
##  1st Qu.: 50.0   1st Qu.: 476.0   1st Qu.:  615.0   1st Qu.: 127.0  
##  Median :107.0   Median : 536.5   Median :  813.5   Median : 159.0  
##  Mean   :105.7   Mean   : 553.0   Mean   :  817.7   Mean   : 246.5  
##  3rd Qu.:150.0   3rd Qu.: 611.0   3rd Qu.:  968.0   3rd Qu.: 249.2  
##  Max.   :343.0   Max.   :3645.0   Max.   :19278.0   Max.   :1898.0  
##                                   NA's   :102                       
##   FIELDING_DP   
##  Min.   : 52.0  
##  1st Qu.:131.0  
##  Median :149.0  
##  Mean   :146.4  
##  3rd Qu.:164.0  
##  Max.   :228.0  
##  NA's   :286

attach(moneyball)
sd(TARGET_WINS)

## [1] 15.75215

sd(BATTING_H)

## [1] 144.5912

sd(BATTING_2B)

## [1] 46.80141

sd(BATTING_3B)

## [1] 27.93856

sd(BATTING_HR)

## [1] 60.54687

sd(BATTING_BB)

## [1] 122.6709

sd(BATTING_SO,na.rm = TRUE)

## [1] 248.5264

sd(BASERUN_SB,na.rm = TRUE)

## [1] 87.79117

sd(BASERUN_CS,na.rm = TRUE)

## [1] 22.95634

sd(BATTING_HBP,na.rm = TRUE)

## [1] 12.96712

sd(PITCHING_H)

## [1] 1406.843

sd(PITCHING_HR)

## [1] 61.29875

sd(PITCHING_BB)

## [1] 166.3574

sd(PITCHING_SO,na.rm = TRUE)

## [1] 553.085

sd(FIELDING_E)

## [1] 227.771

sd(FIELDING_DP, na.rm =TRUE)

## [1] 26.22639

boxplot(TARGET_WINS)

boxplot(BATTING_H)

boxplot(BATTING_2B)

boxplot(BATTING_3B)

boxplot(BATTING_HR)

boxplot(BATTING_BB)

boxplot(BATTING_SO,na.rm = TRUE)

boxplot(BASERUN_SB,na.rm = TRUE)

boxplot(BASERUN_CS,na.rm = TRUE)

boxplot(BATTING_HBP,na.rm = TRUE)

boxplot(PITCHING_H)

boxplot(PITCHING_HR)

boxplot(PITCHING_BB)

boxplot(PITCHING_SO,na.rm = TRUE)

boxplot(FIELDING_E)

boxplot(FIELDING_DP, na.rm =TRUE)

plot_histogram(moneyball)

plot_missing(moneyball)

moneyball[is.na(moneyball$BATTING_SO),"BATTING_SO"] <- median(na.omit(moneyball$BATTING_SO))
moneyball[is.na(moneyball$PITCHING_SO),"PITCHING_SO"] <- median(na.omit(moneyball$PITCHING_SO))
moneyball[is.na(moneyball$BASERUN_SB),"BASERUN_SB"] <- median(na.omit(moneyball$BASERUN_SB))
moneyball[is.na(moneyball$FIELDING_DP),"FIELDING_DP"] <- median(na.omit(moneyball$FIELDING_DP))
moneyball[is.na(moneyball$BASERUN_CS),"BASERUN_CS"] <- median(na.omit(moneyball$BASERUN_CS))
plot_missing(moneyball)

# By observing the data, I see that TEAM_BATTING_H is the sum of 1B, 2B, 3B, HR, to find 1B and analyze them individually, we can find 1B.
attach(moneyball)

## The following objects are masked from moneyball (pos = 3):
## 
##     BASERUN_CS, BASERUN_SB, BATTING_2B, BATTING_3B, BATTING_BB,
##     BATTING_H, BATTING_HBP, BATTING_HR, BATTING_SO, FIELDING_DP,
##     FIELDING_E, INDEX, PITCHING_BB, PITCHING_H, PITCHING_HR,
##     PITCHING_SO, TARGET_WINS

moneyball<- moneyball %>% mutate(BATTING_1B=BATTING_H-BATTING_2B-BATTING_3B,Total_batting=1*BATTING_1B+2*BATTING_2B+3*BATTING_3B+4*BATTING_HR) %>% dplyr ::select(-BATTING_H,-BATTING_HBP,-INDEX)
kable(head(moneyball))

TARGET_WINS	BATTING_2B	BATTING_3B	BATTING_HR	BATTING_BB	BATTING_SO	BASERUN_SB	BASERUN_CS	PITCHING_H	PITCHING_HR	PITCHING_BB	PITCHING_SO	FIELDING_E	FIELDING_DP	BATTING_1B	Total_batting
39	194	39	13	143	842	101	49	9364	84	927	5456	1011	149	1212	1769
70	219	22	190	685	1075	37	28	1347	191	689	1082	193	155	1098	2362
86	232	35	137	602	917	46	27	1377	137	602	917	175	153	1110	2227
70	209	38	96	451	922	43	30	1396	97	454	928	164	156	1140	2056
82	186	27	102	472	920	49	39	1297	102	472	920	138	168	1084	1945
75	200	36	92	443	973	107	59	1279	92	443	973	123	149	1043	1919

cor(moneyball)

##               TARGET_WINS  BATTING_2B   BATTING_3B BATTING_HR  BATTING_BB
## TARGET_WINS    1.00000000  0.28910365  0.142608411  0.1761532  0.23255986
## BATTING_2B     0.28910365  1.00000000 -0.107305824  0.4353973  0.25572610
## BATTING_3B     0.14260841 -0.10730582  1.000000000 -0.6355669 -0.28723584
## BATTING_HR     0.17615320  0.43539729 -0.635566946  1.0000000  0.51373481
## BATTING_BB     0.23255986  0.25572610 -0.287235841  0.5137348  1.00000000
## BATTING_SO    -0.03058135  0.15173438 -0.655709613  0.6930076  0.37148892
## BASERUN_SB     0.12361087 -0.18340432  0.485740156 -0.4068891 -0.04268402
## BASERUN_CS     0.01595982 -0.04584955  0.136181182 -0.2254587 -0.04581766
## PITCHING_H    -0.10993705  0.02369219  0.194879411 -0.2501455 -0.44977762
## PITCHING_HR    0.18901373  0.45455082 -0.567836679  0.9693714  0.45955207
## PITCHING_BB    0.12417454  0.17805420 -0.002224148  0.1369276  0.48936126
## PITCHING_SO   -0.07579967  0.06213042 -0.254238104  0.1774182 -0.02017989
## FIELDING_E    -0.17648476 -0.23515099  0.509778447 -0.5873391 -0.65597081
## FIELDING_DP   -0.03008630  0.25696798 -0.227771884  0.3916524  0.32963974
## BATTING_1B     0.34579395  0.33580405  0.347822719 -0.0318712 -0.12886347
## Total_batting  0.39892151  0.75439415 -0.136638042  0.7493183  0.36408258
##                BATTING_SO  BASERUN_SB  BASERUN_CS  PITCHING_H PITCHING_HR
## TARGET_WINS   -0.03058135  0.12361087  0.01595982 -0.10993705  0.18901373
## BATTING_2B     0.15173438 -0.18340432 -0.04584955  0.02369219  0.45455082
## BATTING_3B    -0.65570961  0.48574016  0.13618118  0.19487941 -0.56783668
## BATTING_HR     0.69300765 -0.40688907 -0.22545867 -0.25014548  0.96937140
## BATTING_BB     0.37148892 -0.04268402 -0.04581766 -0.44977762  0.45955207
## BATTING_SO     1.00000000 -0.21178758 -0.10250193 -0.37571553  0.63286033
## BASERUN_SB    -0.21178758  1.00000000  0.23324171  0.03957227 -0.38005624
## BASERUN_CS    -0.10250193  0.23324171  1.00000000 -0.05259183 -0.22818525
## PITCHING_H    -0.37571553  0.03957227 -0.05259183  1.00000000 -0.14161276
## PITCHING_HR    0.63286033 -0.38005624 -0.22818525 -0.14161276  1.00000000
## PITCHING_BB    0.03498809  0.12928969 -0.04722893  0.32067616  0.22193750
## PITCHING_SO    0.41618159 -0.06424741 -0.05653800  0.26693587  0.19691491
## FIELDING_E    -0.58259305  0.32615276 -0.02917821  0.66775901 -0.49314447
## FIELDING_DP    0.11089804 -0.27023400 -0.10200214 -0.04464784  0.38959550
## BATTING_1B    -0.48464372  0.09474682 -0.01375594  0.33253091  0.04579447
## Total_batting  0.24141254 -0.21340675 -0.16245457 -0.01596413  0.77829405
##                PITCHING_BB  PITCHING_SO  FIELDING_E  FIELDING_DP
## TARGET_WINS    0.124174536 -0.075799674 -0.17648476 -0.030086302
## BATTING_2B     0.178054204  0.062130422 -0.23515099  0.256967975
## BATTING_3B    -0.002224148 -0.254238104  0.50977845 -0.227771884
## BATTING_HR     0.136927564  0.177418187 -0.58733910  0.391652434
## BATTING_BB     0.489361263 -0.020179893 -0.65597081  0.329639737
## BATTING_SO     0.034988093  0.416181592 -0.58259305  0.110898035
## BASERUN_SB     0.129289686 -0.064247407  0.32615276 -0.270234003
## BASERUN_CS    -0.047228927 -0.056538002 -0.02917821 -0.102002137
## PITCHING_H     0.320676162  0.266935871  0.66775901 -0.044647837
## PITCHING_HR    0.221937505  0.196914911 -0.49314447  0.389595503
## PITCHING_BB    1.000000000  0.482172000 -0.02283756  0.192348657
## PITCHING_SO    0.482172000  1.000000000 -0.02332278  0.009552324
## FIELDING_E    -0.022837561 -0.023322782  1.00000000 -0.227394807
## FIELDING_DP    0.192348657  0.009552324 -0.22739481  1.000000000
## BATTING_1B     0.047792487 -0.279280625  0.31333793  0.110655544
## Total_batting  0.182240384 -0.012524535 -0.28654467  0.371830404
##                BATTING_1B Total_batting
## TARGET_WINS    0.34579395    0.39892151
## BATTING_2B     0.33580405    0.75439415
## BATTING_3B     0.34782272   -0.13663804
## BATTING_HR    -0.03187120    0.74931833
## BATTING_BB    -0.12886347    0.36408258
## BATTING_SO    -0.48464372    0.24141254
## BASERUN_SB     0.09474682   -0.21340675
## BASERUN_CS    -0.01375594   -0.16245457
## PITCHING_H     0.33253091   -0.01596413
## PITCHING_HR    0.04579447    0.77829405
## PITCHING_BB    0.04779249    0.18224038
## PITCHING_SO   -0.27928062   -0.01252454
## FIELDING_E     0.31333793   -0.28654467
## FIELDING_DP    0.11065554    0.37183040
## BATTING_1B     1.00000000    0.54607257
## Total_batting  0.54607257    1.00000000

Model 1: Backwards Selection

m1 <-lm(TARGET_WINS~.,data=moneyball)
plot(m1)

summary(m1)

## 
## Call:
## lm(formula = TARGET_WINS ~ ., data = moneyball)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -49.753  -8.626   0.120   8.395  58.561 
## 
## Coefficients: (1 not defined because of singularities)
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   23.6421579  5.3902272   4.386 1.21e-05 ***
## BATTING_2B     0.0279578  0.0073363   3.811 0.000142 ***
## BATTING_3B     0.1133940  0.0159335   7.117 1.48e-12 ***
## BATTING_HR     0.0527325  0.0274915   1.918 0.055219 .  
## BATTING_BB     0.0104483  0.0058377   1.790 0.073621 .  
## BATTING_SO    -0.0084323  0.0025461  -3.312 0.000941 ***
## BASERUN_SB     0.0254236  0.0043565   5.836 6.12e-09 ***
## BASERUN_CS    -0.0110027  0.0157842  -0.697 0.485829    
## PITCHING_H    -0.0008456  0.0003674  -2.302 0.021444 *  
## PITCHING_HR    0.0129626  0.0243894   0.531 0.595135    
## PITCHING_BB    0.0007798  0.0041571   0.188 0.851231    
## PITCHING_SO    0.0028156  0.0009219   3.054 0.002284 ** 
## FIELDING_E    -0.0195325  0.0024609  -7.937 3.23e-15 ***
## FIELDING_DP   -0.1217801  0.0129421  -9.410  < 2e-16 ***
## BATTING_1B     0.0489152  0.0036949  13.239  < 2e-16 ***
## Total_batting         NA         NA      NA       NA    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13.07 on 2261 degrees of freedom
## Multiple R-squared:  0.3154, Adjusted R-squared:  0.3111 
## F-statistic:  74.4 on 14 and 2261 DF,  p-value: < 2.2e-16

Model 2 Get rid of “Total_Batting” for now

m2 <- update(m1,~.-Total_batting)
plot(m2)

summary(m2)

## 
## Call:
## lm(formula = TARGET_WINS ~ BATTING_2B + BATTING_3B + BATTING_HR + 
##     BATTING_BB + BATTING_SO + BASERUN_SB + BASERUN_CS + PITCHING_H + 
##     PITCHING_HR + PITCHING_BB + PITCHING_SO + FIELDING_E + FIELDING_DP + 
##     BATTING_1B, data = moneyball)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -49.753  -8.626   0.120   8.395  58.561 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 23.6421579  5.3902272   4.386 1.21e-05 ***
## BATTING_2B   0.0279578  0.0073363   3.811 0.000142 ***
## BATTING_3B   0.1133940  0.0159335   7.117 1.48e-12 ***
## BATTING_HR   0.0527325  0.0274915   1.918 0.055219 .  
## BATTING_BB   0.0104483  0.0058377   1.790 0.073621 .  
## BATTING_SO  -0.0084323  0.0025461  -3.312 0.000941 ***
## BASERUN_SB   0.0254236  0.0043565   5.836 6.12e-09 ***
## BASERUN_CS  -0.0110027  0.0157842  -0.697 0.485829    
## PITCHING_H  -0.0008456  0.0003674  -2.302 0.021444 *  
## PITCHING_HR  0.0129626  0.0243894   0.531 0.595135    
## PITCHING_BB  0.0007798  0.0041571   0.188 0.851231    
## PITCHING_SO  0.0028156  0.0009219   3.054 0.002284 ** 
## FIELDING_E  -0.0195325  0.0024609  -7.937 3.23e-15 ***
## FIELDING_DP -0.1217801  0.0129421  -9.410  < 2e-16 ***
## BATTING_1B   0.0489152  0.0036949  13.239  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13.07 on 2261 degrees of freedom
## Multiple R-squared:  0.3154, Adjusted R-squared:  0.3111 
## F-statistic:  74.4 on 14 and 2261 DF,  p-value: < 2.2e-16

Model 3: get rid of variables are not statistically significant.

m3<-update(m1,~.-BATTING_HR-BATTING_BB-BASERUN_CS-PITCHING_HR-PITCHING_BB)
plot(m3)

summary(m3)

## 
## Call:
## lm(formula = TARGET_WINS ~ BATTING_2B + BATTING_3B + BATTING_SO + 
##     BASERUN_SB + PITCHING_H + PITCHING_SO + FIELDING_E + FIELDING_DP + 
##     BATTING_1B + Total_batting, data = moneyball)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -51.028  -8.645   0.079   8.538  58.554 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   28.5417286  4.9219496   5.799 7.61e-09 ***
## BATTING_2B    -0.0094319  0.0094685  -0.996 0.319289    
## BATTING_3B     0.0605133  0.0156578   3.865 0.000114 ***
## BATTING_SO    -0.0099177  0.0024244  -4.091 4.45e-05 ***
## BASERUN_SB     0.0298779  0.0039613   7.542 6.64e-14 ***
## PITCHING_H    -0.0008022  0.0003216  -2.494 0.012699 *  
## PITCHING_SO    0.0029552  0.0006735   4.388 1.20e-05 ***
## FIELDING_E    -0.0226345  0.0021496 -10.530  < 2e-16 ***
## FIELDING_DP   -0.1125269  0.0126728  -8.879  < 2e-16 ***
## BATTING_1B     0.0284034  0.0050326   5.644 1.87e-08 ***
## Total_batting  0.0191872  0.0023145   8.290  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13.1 on 2265 degrees of freedom
## Multiple R-squared:  0.3115, Adjusted R-squared:  0.3085 
## F-statistic: 102.5 on 10 and 2265 DF,  p-value: < 2.2e-16

Model4: Keep getting rid of the insignificant variable.

m4 <-update(m3,~.-BATTING_2B-PITCHING_H)
plot(m4)

summary(m4)

## 
## Call:
## lm(formula = TARGET_WINS ~ BATTING_3B + BATTING_SO + BASERUN_SB + 
##     PITCHING_SO + FIELDING_E + FIELDING_DP + BATTING_1B + Total_batting, 
##     data = moneyball)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -52.140  -8.690   0.037   8.443  59.016 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   27.996811   4.798551   5.834 6.17e-09 ***
## BATTING_3B     0.069968   0.015282   4.578 4.94e-06 ***
## BATTING_SO    -0.007831   0.002273  -3.446 0.000580 ***
## BASERUN_SB     0.030903   0.003936   7.851 6.33e-15 ***
## PITCHING_SO    0.002079   0.000591   3.517 0.000444 ***
## FIELDING_E    -0.025823   0.001712 -15.081  < 2e-16 ***
## FIELDING_DP   -0.111019   0.012628  -8.792  < 2e-16 ***
## BATTING_1B     0.028995   0.004849   5.979 2.60e-09 ***
## Total_batting  0.017044   0.001734   9.830  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13.11 on 2267 degrees of freedom
## Multiple R-squared:  0.3093, Adjusted R-squared:  0.3068 
## F-statistic: 126.9 on 8 and 2267 DF,  p-value: < 2.2e-16

Evaluation by using our model

evaluation <- read.csv("https://raw.githubusercontent.com/xkong100/data-621/master/Hw1/moneyball-evaluation-data.csv", stringsAsFactors = FALSE, check.names = FALSE, na.strings = c("", "NA"))
kable(head(evaluation))

INDEX	TEAM_BATTING_H	TEAM_BATTING_2B	TEAM_BATTING_3B	TEAM_BATTING_HR	TEAM_BATTING_BB	TEAM_BATTING_SO	TEAM_BASERUN_SB	TEAM_BASERUN_CS	TEAM_BATTING_HBP	TEAM_PITCHING_H	TEAM_PITCHING_HR	TEAM_PITCHING_BB	TEAM_PITCHING_SO	TEAM_FIELDING_E	TEAM_FIELDING_DP
9	1209	170	33	83	447	1080	62	50	NA	1209	83	447	1080	140	156
10	1221	151	29	88	516	929	54	39	NA	1221	88	516	929	135	164
14	1395	183	29	93	509	816	59	47	NA	1395	93	509	816	156	153
47	1539	309	29	159	486	914	148	57	42	1539	159	486	914	124	154
60	1445	203	68	5	95	416	NA	NA	NA	3902	14	257	1123	616	130
63	1431	236	53	10	215	377	NA	NA	NA	2793	20	420	736	572	105

evaluation <- cleanNames(evaluation)
kable(head(evaluation))

INDEX	BATTING_H	BATTING_2B	BATTING_3B	BATTING_HR	BATTING_BB	BATTING_SO	BASERUN_SB	BASERUN_CS	BATTING_HBP	PITCHING_H	PITCHING_HR	PITCHING_BB	PITCHING_SO	FIELDING_E	FIELDING_DP
9	1209	170	33	83	447	1080	62	50	NA	1209	83	447	1080	140	156
10	1221	151	29	88	516	929	54	39	NA	1221	88	516	929	135	164
14	1395	183	29	93	509	816	59	47	NA	1395	93	509	816	156	153
47	1539	309	29	159	486	914	148	57	42	1539	159	486	914	124	154
60	1445	203	68	5	95	416	NA	NA	NA	3902	14	257	1123	616	130
63	1431	236	53	10	215	377	NA	NA	NA	2793	20	420	736	572	105

plot_missing(evaluation)

evaluation[is.na(evaluation$BATTING_SO),"BATTING_SO"] <- median(na.omit(evaluation$BATTING_SO))
evaluation[is.na(evaluation$PITCHING_SO),"PITCHING_SO"] <- median(na.omit(evaluation$PITCHING_SO))
evaluation[is.na(evaluation$BASERUN_SB),"BASERUN_SB"] <- median(na.omit(evaluation$BASERUN_SB))
evaluation[is.na(evaluation$FIELDING_DP),"FIELDING_DP"] <- median(na.omit(evaluation$FIELDING_DP))
evaluation[is.na(evaluation$BASERUN_CS),"BASERUN_CS"] <- median(na.omit(evaluation$BASERUN_CS))
plot_missing(evaluation)

attach(evaluation)

## The following objects are masked from moneyball (pos = 3):
## 
##     BASERUN_CS, BASERUN_SB, BATTING_2B, BATTING_3B, BATTING_BB,
##     BATTING_H, BATTING_HBP, BATTING_HR, BATTING_SO, FIELDING_DP,
##     FIELDING_E, INDEX, PITCHING_BB, PITCHING_H, PITCHING_HR,
##     PITCHING_SO

## The following objects are masked from moneyball (pos = 4):
## 
##     BASERUN_CS, BASERUN_SB, BATTING_2B, BATTING_3B, BATTING_BB,
##     BATTING_H, BATTING_HBP, BATTING_HR, BATTING_SO, FIELDING_DP,
##     FIELDING_E, INDEX, PITCHING_BB, PITCHING_H, PITCHING_HR,
##     PITCHING_SO

evaluation<- evaluation %>% mutate(BATTING_1B=BATTING_H-BATTING_2B-BATTING_3B,Total_batting=1*BATTING_1B+2*BATTING_2B+3*BATTING_3B+4*BATTING_HR) %>% dplyr ::select(-BATTING_H,-BATTING_HBP,-INDEX)
kable(head(evaluation))

BATTING_2B	BATTING_3B	BATTING_HR	BATTING_BB	BATTING_SO	BASERUN_SB	BASERUN_CS	PITCHING_H	PITCHING_HR	PITCHING_BB	PITCHING_SO	FIELDING_E	FIELDING_DP	BATTING_1B	Total_batting
170	33	83	447	1080	62	50.0	1209	83	447	1080	140	156	1006	1777
151	29	88	516	929	54	39.0	1221	88	516	929	135	164	1041	1782
183	29	93	509	816	59	47.0	1395	93	509	816	156	153	1183	2008
309	29	159	486	914	148	57.0	1539	159	486	914	124	154	1201	2542
203	68	5	95	416	92	49.5	3902	14	257	1123	616	130	1174	1804
236	53	10	215	377	92	49.5	2793	20	420	736	572	105	1142	1813

pred <- predict(m1, evaluation,type='response')

## Warning in predict.lm(m1, evaluation, type = "response"): prediction from a
## rank-deficient fit may be misleading

final <- data.frame(cbind(pred, moneyball$TARGET_WINS))

## Warning in cbind(pred, moneyball$TARGET_WINS): number of rows of result is
## not a multiple of vector length (arg 1)

kable(head(final))

pred	V2
64.02285	39
65.73235	70
75.27890	86
85.74341	70
66.37855	82
69.83817	75